Optimizing completion operations

ABSTRACT

A system for optimizing a completion operation includes an interface to equipment and sensors for performing the completion operation. The interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function. The short-term cost function includes a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost.

BACKGROUND

When performing an oilfield operation, decisions are often complexbecause of the large number and kinds of considerations to be taken intoaccount, including the uncertainty of the risks and rewards that mayonly be discovered during the operation. Risk and reward analysis is animportant part of the decision-making process for oil exploration andproduction for several reasons. First, risk and reward analysis providesa means for prioritizing the large number and kinds of decisions. Next,risk and reward analysis provides an approach for balancing valuetradeoffs and different preferences of the stakeholders in the decisionprocess. For example, a balance may be achieved between the conflictinggoals of drilling as fast as possible, maintaining integrity of theformation, and ensuring on-site safety.

Current modeling of risks and rewards lacks accuracy and flexibility inthe face of changing conditions. Specifically, unexpected high-riskevents are addressed in an ad-hoc manner during the operation, andunexpected rewards associated with little risk are not pursued.

BRIEF DESCRIPTION OF THE DRAWINGS

Accordingly, there are disclosed herein certain oilfield operationoptimization systems and methods. In the following detailed descriptionof the various disclosed embodiments, reference will be made to theaccompanying drawings in which:

FIG. 1 is a contextual view of an illustrative drilling environment;

FIG. 2 is a contextual view of an illustrative logging and cementingenvironment;

FIG. 3 is a contextual view of an illustrative hydraulic fracturingenvironment;

FIGS. 4 is a contextual view of an illustrative well completionenvironment;

FIG. 5 is a contextual view of an illustrative formation treatmentenvironment;

FIG. 6A is an illustration of a user interfacing with an illustrativerisk and reward optimization system;

FIGS. 6B and 6C are a block diagrams of illustrative risk and rewardoptimization systems;

FIG. 7 is block diagram of an illustrative control module configurationin a risk and reward optimization system;

FIG. 8 is flow diagram of an illustrative risk and reward optimizationmethod; and

FIGS. 9A-9C are diagrams of illustrative risk and reward scenarios.

It should be understood, however, that the specific embodiments given inthe drawings and detailed description thereto do not limit thedisclosure. On the contrary, they provide the foundation for one ofordinary skill to discern the alternative forms, equivalents, andmodifications that are encompassed together with one or more of thegiven embodiments in the scope of the appended claims.

NOTATION AND NOMENCLATURE

Certain terms are used throughout the following description and claimsto refer to particular system components and configurations. As oneskilled in the art will appreciate, companies may refer to a componentby different names. This document does not intend to distinguish betweencomponents that differ in name but not function. In the followingdiscussion and in the claims, the terms “including” and “comprising” areused in an open-ended fashion, and thus should be interpreted to mean“including, but not limited to . . . ”. Also, the term “couple” or“couples” is intended to mean either an indirect or a direct electricalconnection. Thus, if a first device couples to a second device, thatconnection may be through a direct electrical connection, or through anindirect electrical connection via other devices and connections. Inaddition, the term “attached” is intended to mean either an indirect ora direct physical connection. Thus, if a first device attaches to asecond device, that connection may be through a direct physicalconnection, or through an indirect physical connection via other devicesand connections.

DETAILED DESCRIPTION

The issues identified in the background are at least partly addressed bysystems and methods for optimizing oilfield operations. FIG. 1 shows anillustrative drilling environment. A drilling platform 2 supports aderrick 4 having a traveling block 6 for raising and lowering abottomhole assembly (BHA) 19. The platform 2 may also be locatedoffshore for subsea drilling purposes in at least one embodiment. TheBHA 19 may include one or more of a rotary steerable system, loggingwhile drilling system, drill bit 14, reamer, and downhole motor 26. Atop drive 10 supports and rotates the BHA 19 as it is lowered throughthe wellhead 12. The drill bit 14 and reamer may also be driven by thedownhole motor 26. As the drill bit 14 and reamer rotate, they create aborehole 17 that passes through various formations 18. A pump 20circulates drilling fluid 24 through a feed pipe 22, through theinterior of the drill string to the drill bit 14. The fluid exitsthrough orifices in the drill bit 14 and flows upward to transport drillcuttings to the surface where the fluid is filtered and recirculated.

A data processing system 50 may be coupled to a measurement unit on theplatform 2, and may periodically obtain data from the measurement unitas a function of position and/or time. Software (represented byinformation storage media 52) may run on the data processing system 50to collect the data and organize it in a file or database. The softwaremay respond to user input via a keyboard 54 or other input mechanism todisplay data as an image or movie on a monitor 56 or other outputmechanism. The software may process the data to optimize oilfieldoperations as described below.

In running operations such as drilling operations, short-term rewardsmay include weight-on-bit (WOB) and drillstring rotations-per-minute(RPM). A higher weight on the bit 14 and faster RPM are preferable asboth are factors in increasing the rate of penetration (ROP) into theformation 18. The short-term risks may include a region of vibration.Specifically, as the WOB and RPM increase, vibrations in the drillstringbecome increasingly likely. These vibrations interfere with thestructural integrity of the drillstring components and also add noise tothe drilling system. The long-term rewards may include an increase inROP. The long-term rewards may also include a decrease in maximum doglegseverity. A dogleg is a bend in the borehole 17. Dogleg severity is ameasure of the amount of change in the inclination, and/or azimuth ofthe borehole 17. By decreasing the dogleg severity, the strain on thedrillstring and other downhole components is decreased.

In fluid management operations such as drilling fluid operations,long-term risks may include formation damage if the drilling fluid 24does not prevent undesired formation fluid from entering the borehole17. Such formation damage may occur if the composition and density ofthe drilling fluid 24 is not tailored to the formation. Long-termrewards may include lowering the cost of drilling fluid, e.g., bychanging the composition of the drilling fluid 24 to use cheaperingredients, but still tailoring the drilling fluid 24 to the formation18. Long-term rewards may also include increasing the production rate ofdrilling fluid, i.e., increasing the rate at which useable drillingfluid is available.

In running operations such as hydraulic workover operations, where anunderperforming well is reworked with hydraulic workover pipe toincrease performance, short-term rewards may include increased hydraulicworkover pipe insertion speed. The short-term risks may include releaseof downhole pressure during the workover operation. The long-termrewards may include increased hydraulic workover pipe insertion speed,which reduces the total time needed for the hydraulic workoveroperation.

FIG. 2 shows an illustrative logging and cementing environment. Assections of the borehole 212 are completed, the drill string may beremoved from the borehole 212 and replaced by a casing string 200. Acement slurry is pumped into the annular space between the casing string200 and the wall of the borehole 212, and the slurry hardens to form acement sheath 201. Ideally, the cement slurry displaces the drillingfluid and other materials from the annulus to form a continuous sheaththat binds to the formation and tubing to seal the annulus against fluidflow. Various cement slurry compositions have been developed to providevarious desirable features such as a density that can be tailored toavoid damage to the formation, a viscosity that is low enough tofacilitate pumping and high enough to minimize mixing with other fluids,an ability to bind to the formation and casing material, and in someinstances, a “self-healing” ability to seal any cracks that develop.Certain cement resin formulations offer an extremely adjustable set ofproperties.

Once the cementing job has been completed (i.e., the slurry has beenpumped into position and allowed to set), a wireline logging suite istypically employed to evaluate the sheath and verify that the desiredplacement and sheath quality have been achieved. For example, a cementcrew may verify that the previous materials have been displaced in theregions where formation fluid inflows might otherwise occur and thatthere are no bubbles, gaps, or flow paths along the sheath.

Next, a logging truck 202 may suspend a wireline logging sonde 204 on awireline cable 206 having conductors for transporting power to the sondeand telemetry from the sonde to the surface. On the surface, a computer208 acquires and stores measurement data from the logging tools in thesonde 204 as a function of position along the borehole and as a functionof azimuth. The illustrated sonde 204 includes an ultrasonic scanningtool 216 and a cement bond logging (CBL) tool having an omnidirectionalsource 218, an acoustic isolator 220, an azimuthally-sensitive receiver222, and an omnidirectional receiver 224. Centralizers 210 keep thesonde centered. The wireline sonde may further include an orientationmodule and a control/telemetry module for coordinating the operations ofthe various tools and communications between the various instruments andthe surface. The ultrasonic scanning tool 216 has a rotating transceiverhead that transmits ultrasonic pulses and receives reflected pulses toand from many points on the inner circumference of the casing. Theamplitudes of the initial reflection from the inner surface of thecasing and subsequent reflections from the outer surface of the casingand acoustic interfaces beyond the casing are indicative of the acousticimpedances of the casing and the annular materials beyond the casing.The acoustic interfaces can be mapped by tracking the travel time ofeach reflection. The CBL tool uses the acoustic source 218 to generateacoustic pulses that propagate along the casing string. The acousticisolator 220 suppresses propagation of acoustic signals through thesonde itself. The receivers 222 and 224 detect the waveforms of thepropagating acoustic signals, which have characteristics indicative ofthe quality of the cement sheath. For example, the maximum amplitude ofthe waveforms relative to the transmitted pulse varies with the qualityof the bond between the casing and the cement.

In completion operations such as cementing operations, a short-termreward may include increased cement pumping rate, which decreases thetotal time needed for a cement job. The short-term risks may includeformation of a bubble, uneven cement surface, and poor cement bond,which decrease the integrity of the cement. The long-term risks mayinclude complete loss of cement integrity and low fracture gradient,which is the pressure required to fracture the cement. The long-termrewards may include increased cement integrity, decreased wait-on-cementtime, and decreased material cost. For example, the type or formulationof cement may be tailored to the cement operation to decrease materialcost.

In running operations such as logging operations, short-term risks mayinclude increased measurement noise, which is undesirable becauseincreased noise decreases the accuracy of the logging data. Short-termrisks may also include biased measurements, which may result inpersistent errors in the logging data. The long-term risks may includecreation of inaccurate formation models and inaccurate reservoir modelsafter completion of the logging operations. The long-term rewards mayinclude increased logging speed, which decreases the total time neededfor the logging operations, and increased logging resolution, whichincreases the accuracy of the logging data and models.

FIG. 3 shows an illustrative hydraulic fracturing environment in which aborehole 302 has been drilled into the target formation 300. Theborehole 302 has been cased with a casing 304 and cemented to sustainthe structural integrity and stability of the borehole 302. The targetformation 300 may include multiple layers, each layer with a differenttype of rock formation, including the hydrocarbon-containing targetformation within which the borehole may extend horizontally for somedistance. The casing 304 contains multiple perforations 306 throughwhich a fracturing fluid, such as water, is injected at high pressureinto the target formation. This high-pressure fluid injection createsand opens fractures 308 that extend through the target formation. Thehigh-pressure fluid may contain additional chemicals and materials, suchas a proppant material (e.g., sand) that maintains the structuralstability of the fractures and prevents the fractures from fullycollapsing. Typically, the horizontal portions of the borehole aredrilled generally parallel to the direction of maximum stress, causingthe fractures to propagate generally perpendicular to the borehole. (Asfractures tend to propagate perpendicular to the direction of maximumstress, such propagation may be expected to occur at a predictable anglefrom the borehole axis when the borehole is not aligned with the maximumstress direction.) The overlying and underlying formation layers tend toresist fracture propagation, consequently fractures tend to propagatelaterally within the target formation, to a length that depends on therate and volume of the injected fracturing fluid. Thus, each fracturehas a length 310 relative to the casing 304. Each fracture also has aninitiation location 314 determined by the perforation position, which istypically measured relative to the distal end of the borehole 302. Whereregular spacing is employed, the perforations (and hence the fractureinitiation points) have a fixed spacing 312 between them.

In stimulation operations such as hydraulic fracturing operations, thelong-term risks may include unsuitable locations for hydraulicfracturing, e.g., if the formation 300 includes material that resistsfracturing. The long-term risks may also include incompatibility betweenfracturing fluid and formation, wherein the proppants are ineffective orfracturing fluid damages or contaminates the formation, and proppantscreen out, wherein the proppant prevents the desired hydrocarbons fromentering the borehole.

FIG. 4 shows an illustrative well completion environment. Specifically,FIG. 4 shows an example of a producer well with a borehole 402 that hasbeen drilled into the earth. The producer well also includes a casingheader 404 and a casing 406, both secured into place by cement 403. Ablowout preventer (BOP) 408 couples to the casing header 404 and to aproduction wellhead 410, which together seal in the well head and enablefluids to be extracted from the well in a safe and controlled manner

Measured well data is periodically sampled and collected from theproducer well and combined with measurements from other wells within areservoir, enabling the overall state of the reservoir to be monitoredand assessed. These measurements may be taken using a number ofdifferent downhole and surface instruments, including but not limitedto, a temperature and pressure sensor 418 and a flow meter 420.Additional devices also coupled in line to a production tubing 412include a downhole choke 416 (used to vary the fluid flow restriction),an electric submersible pump (ESP) 422 (which draws in fluid flowingfrom perforations 425 outside the ESP 422 and production tubing 412), anESP motor 424 (to drive the ESP 322), and a packer 414 (isolating theproduction zone below the packer from the rest of the well). Additionalsurface measurement devices may be used to measure, for example, thetubing head pressure and the electrical power consumption of the ESPmotor 424.

Each of the devices along the production tubing 412 couples to a cable428, which is attached to the exterior of the production tubing 412 andis run to the surface through the blowout preventer 408 where it couplesto a control panel 432. The cable 428 provides power to the devices towhich it couples, and further provides signal paths (electrical,optical, etc.) that enable control signals to be directed to thedownhole devices, and for measurement signals to be received at thesurface from the downhole devices. The devices may be controlled andmonitored locally by field personnel using a user interface built intothe control panel 432 coupled to an oilfield optimization system.Communication between control panel 432 and the oilfield optimizationsystem may be via a wireless network (e.g., a cellular network), via acabled network (e.g., a cabled connection to the Internet), or acombination of wireless and cabled networks.

If the formation 450 contains loose particulates 452 such as sands orsoft sandstone, the particulates may migrate into the borehole throughthe perforations 425, clogging the production system and eroding thedevices along the production tubing 412. To prevent this, fluid may beinjected into the formation, and the fluid may react chemically or withheat to produce a permeable gel or solid to block the particulates 452while allowing fluid flow. Also, a porous screen may be placed in theborehole between the production tubing 412 and the formation 450 wall.This technique is commonly referred to as gravel packing and the screenmay include certain size rocks or gravel, Ottawa sand, walnut shells,glass beads, and the like.

In completion operations such as well completion operations, short-termrewards may include gravel-packing sand transport speed. A fastertransport speed means that sand can be deployed to the annulus faster,thus decreasing total job time. Short-term risks may include thingravel-packing carrier fluid and a sand dune effect, which occurs whenan accumulation of sand decreases the production flow rate. Thelong-term risks may include damage to the reservoir. For example, thesands may enter the formation or reservoir and clog the conduit, therebypreventing hydrocarbons from escaping. The long-term rewards may includeincreased sand screening efficiency, wherein the porous screen or gelbecomes more efficient, and an increase in borehole integrity.

FIG. 5 shows an illustrative formation treatment environment. While FIG.5 depicts a land-based system, like systems may be operated in subsealocations as well. A treatment fluid may be formulated in a mixing tank502. The treatment fluid may be conveyed via line 504 to a wellhead 506,where the treatment fluid enters a tubular 508 extending from wellhead506 into subterranean formation 510. Upon being ejected from tubular508, the treatment fluid may subsequently penetrate into subterraneanformation 510. Pump 512 may be configured to raise the pressure of thetreatment fluid to a desired degree before its introduction into tubular508. Various additional components may be present that have notnecessarily been depicted in FIG. 5 in the interest of clarity.Non-limiting additional components that may be present include, but arenot limited to, supply hoppers, valves, condensers, adapters, joints,gauges, sensors, compressors, pressure controllers, pressure sensors,flow rate controllers, flow rate sensors, temperature sensors, and thelike. Although not depicted in FIG. 5, the treatment fluid may, in someembodiments, flow back to wellhead 506 and exit subterranean formation510. In some embodiments, the treatment fluid that has flowed back towellhead 506 may subsequently be recovered and recirculated tosubterranean formation 510.

In fluid management operations such as production chemical operations,the short-term rewards may include an increase in production rate of thetreatment fluid and increase in damage removal rate. For example, scalemay appear at the wellbore, restricting the hydrocarbon flow. Pumping anacid into the wellbore can dissolve such scale. The short-term risks mayinclude a temperature change downhole, requiring reformulation of thetreatment fluid, and a change in the composition of the reservoir fluid.The long-term risks may include clogging of the borehole, and thelong-term rewards may include increased separation capability ofreservoir fluids, e.g. separation of water and oil, increase inproduction rate, and decrease in treatment fluid, or chemical, cost.

As shown in FIG. 6A, an analyst may employ a user interface 679 of aworkstation 604 to view and/or control the optimization process. Theworkstation 604 is part of the hardware platform of an oilfieldoperation optimization system such as that shown in FIG. 6B. Theillustrative hardware platform couples the workstation 604 to one ormore multi-processor computers 606 via a local area network (LAN) 605.The one or more multi-processor computers 606 are in turn coupled via astorage area network (SAN) 608 to one or more shared storage units 610.Using the personal workstation 604, the analyst is able to load sensorand control data into the system, and to configure and monitor theprocessing of the sensor and control data.

Personal workstation 604 may take the form of a desktop computer with adisplay that shows graphical representations of the input and resultdata, and with a keyboard that enables the user to move files andexecute processing software. LAN 605 provides high-speed communicationbetween multi-processor computers 606 and with personal workstation 604.The LAN 605 may take the form of an Ethernet network.

Multi-processor computer(s) 606 provide parallel processing capabilityto enable suitably prompt processing of the input data and measurementsignals to derive the results data and control signals. Each computer606 includes multiple processors 612, distributed memory 614, aninternal bus 616, a SAN interface 618, and a LAN interface 620. Eachprocessor 612 operates on allocated tasks to solve a portion of theoverall optimization problem and contribute to at least a portion of theoverall results. Associated with each processor 612 is a distributedmemory module 614 that stores application software and a working dataset for the processor's use. Internal bus 616 provides inter-processorcommunication and communication to the SAN or LAN networks via thecorresponding interfaces 618, 620. Communication between processors indifferent computers 606 can be provided by LAN 605.

SAN 608 provides high-speed access to shared storage devices 610. TheSAN 608 may take the form of, e.g., a Fibrechannel or Infinibandnetwork. Shared storage units 610 may be large, stand-alone informationstorage units that employ magnetic disk media for nonvolatile datastorage. To improve data access speed and reliability, the sharedstorage units 610 may be configured as a redundant disk array (“RAID”).

One or more cores 612 may make up a control module as shown in FIG. 6C.The control module 650 may receive measurement signals from sensors 652that monitor the oilfield operation 658, and the control module 650 maysend control signals to equipment 656 performing the oilfield operation658. The control module 650 may read from and write to a model of theoilfield operation stored in memory. In at least one embodiment, thecontrol module includes cores and memory, and as such, the controlmodule 650 itself includes the model 654. The control module may beremotely coupled to the LAN 605 for communication purposes.

FIG. 7 shows an illustrative control module 650 for oilfield operationoptimization. The control module 650 includes a long-term optimizer 702coupled to one or more short-term optimizers 704, 706. Each short-termoptimizer 704, 706 controls one or more sub-processes 708, 710 of theoilfield operation, and measurement signals from the sub-processes 708,710 are fed back into the long-term optimizer 702, e.g. to update theoperation model, and short-term optimizers 704, 706, e.g. to update theactual job state. The long-term optimizer 702 constrains the short-termoptimizers 704, 706, e.g. with a desired job state, and the shortterm-optimizers 704, 706 control the oilfield operation within thoseconstraints.

The long-term optimizer 702 maximizes a long-term cost function thatincludes one or more terms representing long-term rewards and one ormore terms representing long-term risks. The long-term risks are static,or slow to change, over the job. Should a short-term risk persist over athreshold time period, the short-term risk may be promoted to along-term risk. By maximizing the long-term cost function, the long-termoptimizer 702 calculates an optimal design and passes the design to theshort-term optimizers 704, 706. The long-term cost function may be inthe following form:max {long-term rewards−costs+ρ·long-term risks}  (1)where ρ is a weighting factor balancing the long-term risks. Equation(1) for a particular oilfield operation, e.g., increasing the stimulatedreservoir volume (SRV) in a hydraulic fracturing environment, may takethe following form:max {SRV−material cost−supply chain cost+ρ·long-term risks}  (2)subject to

SRV=f₁ (DV)

material cost=f₂ (DV)

supply chain cost=f₃ (DV)

long-term risks=f₄ (DV)

where DV are decision variables. Similarly, equations may be formed forother oilfield operations including long-term rewards, costs, andlong-term risks unique to those operations and discussed below. Due tothe large number of decision variables, the long-term optimizer may onlyrun every several hours or every several stages of the oilfieldoperation. The long-term optimizer may include one or more models thatsupplies the decision variables. For example, for a hydraulic fracturingoperation, the models may include a Perkins-Kern-Nordgen model, areservoir model, and a surface equipment model. The models onlycalculate final steady-state values of the variables and theintermediate responses are ignored in at least one embodiment. Theinputs to the models may include measurement signals from thesub-processes 708, 710.

The short-term optimizer modules 704, 706 minimize a short-term costfunction according to constraints, the optimized decision variables,from the long-term optimizer 702. The short term cost function may takethe following form:min {ρ_(0·∥)J_(act)−J_(des)∥²+ρ₁·short-term rewards+ρ₂·short-termrisks}  (3)subject to

J_(des)=g₁ (DV_(optimal))

J_(act)=g₂ (job state)

short-term risks=g₃ (job state)

where the vector J_(des) contains the model design goals computed by thelong-term optimizer and from the optimal long-term decision variablesDV_(optimal), and where the vector J_(act) represents the current actualstate of the job. ρ₀, ρ₁ and ρ₂ are the weighting factors balancing themodel-based control and risk-reward control. Short-term risks arequick-to-change risks that only exist in a local operational region overa short temporal window. The short-term optimizer modules 704, 706 mayderive the current job state based on the measurement signals receivedand the control signals sent, and deriving the current job state may beperformed with an adaptive system model. The control module 650 mayallocate portions of risk between the short-term cost function and thelong-term cost function based on dynamic variability of those portions.

After minimizing the short-term cost function, the short-term optimizers704, 706 send control signals based on the minimization to the equipmentperforming the oilfield operation, and the short-term optimizerscontinue to receive measurement data regarding the operation fromsensors. The control signals may automatically, i.e. without humaninput, adjust the equipment to balance risk and reward based on theshort-term cost function as constrained by the long-term cost function.Measurement data is fed back to the short-term optimizers 704, 706 aswell as the long-term optimizer 702 as measurement signals for updatingthe model, updating the job state, and the like. The short-termoptimizers 704, 706 may also send data to the long-term optimizer 702 toadjust the model design goals. The long-term cost function may include acoordination term to balance the rewards and risks among severalshort-term optimizers 704, 706.

The maximization of the long-term cost function can be computationallyintensive, and as such, it may be computed less frequently thanminimization of the short-term cost function, which may be computedcontinuously and in real time.

The model of the oilfield operation may take the form:x(k+1)=Ax(k)+Bu(k)+w+(k)   (4)y(k)=Cx(k)+v(k)   (5)where matrices A, B and C are the operation matrices and can betime-varying, and vector x(k) is the internal state of the operation.The vectors u(k) and y(k) are the input and output vectors of theoperation, respectively. The process noise w(k) and measurement noisev(k) are considered to have a Gaussian distribution with covariancematrices W and V, respectively. The values of W and V can be fromsupplied from a user, learned from data, or measured directly bysensors. From Kalman filtering theory, the total uncertainty of theoutput y(k) can be represented byΣ_(x)(k+1)=AΣ _(x)(k)A ^(T) +W−AΣ _(x)(k)C ^(T)(CΣ _(x)(k)C ^(T) +V)⁻¹CΣ _(x)(k)A ^(T)   (6)Σ_(y)(k)=CΣ _(x)(k)C ^(T) +V(k)   (7)where Σx is the uncertainty matrix of state vector x(k), Σy is theuncertainty matrix of output y(k), and the diagonal elements of Σ_(y)are the uncertainty for individual decision variables. For example, in ahydraulic fracturing environment, the input vector may be u(k)=[F(k)c_(p)(k)]^(T), where F(k) is the pump rate and c_(p)(k) is the proppantconcentration. The output vector may be y(k)=[L(k) w(k)]^(T), where L(k)and w(k) are fracture length and width, respectively. The first andsecond diagonal elements, Σ_(y,11) and Σ_(y,22), of the uncertaintymatrix Σy provide an estimate of current uncertainty of fracturedimensions.

The weighting factors in the short-term optimizers 704, 706 may beadjusted dynamically according to the output uncertainty Σy. If theuncertainty level is low, for example if Σ_(y,11) and Σ_(y,22), thediagonal elements of Σy, are smaller than 10% of the values ofcorresponding variables y₁ and y₂ (e.g., L(k) and w(k) in the example inthe previous paragraph), the system may be operated in model-basedcontrol mode, which may be interpreted as a setpoint tracking objective,and thus the weighting factors are set as ρ₀=1, ρ₁=0 and ρ₂=0. If theuncertainty level is high, for example if Σ_(y,11) and Σ_(y,22) arecomparable with corresponding variables y₁ and y₂, the system may beoperated in risk-reward control mode, leading to ρ_(o)=1, ρ₁=1 and ρ₂=1.If the uncertainty level is neither high nor low, for example ifΣ_(y,11) and Σ_(y,22) are between 10% and 100% of correspondingvariables y₁ and y₂, then the system may be operated in a hybrid ofmodel-based control mode and risk-reward control mode, and the weightsmay be determined by the uncertainty. For example, if Σ_(y,11)/y₁=0.5and Σ_(y,11)/y₁=0.3, the average value of uncertainty-to-signal ratio is0.4. Based on this value, the weighting factors may be chosen as ρ₀=0.6,ρ₁=0.4 and ρ₂=0.4, meaning that 60% of the control effort is based onmodel-based control mode while 40% of the control effort is based onrisk-reward control mode.

In hydraulic fracturing operations, the control signals may control oraffect perforation density, borehole diameter, casing diameter,perforation diameter, fracturing fluid composition, proppantcomposition, gel breaker composition, pump rate, and proppant schedule.The long-term risks may include unsuitable locations for hydraulicfracturing, incompatibility between fracturing fluid and formation, andproppant screen out. The measurement signals of the hydraulic fracturingoperation may include pressure and microseismic activity.

In drilling fluid operations, the control signals may affect or controlpump rate, drilling fluid composition, fluid addition rate, rock cuttingremoval rate, and monitoring equivalent circulating density. Thelong-term risks may include equivalent circulating density belowleak-off test and formation damage. Long-term rewards may include costof drilling fluid and production rate of drilling fluid.

In well completion operations, short-term rewards may include includinggravel-packing sand transport speed. Short-term risk may include thingravel-packing carrier fluid and sand dune effect. Control signals maycontrol or affect pump rate, gravel-packing sand concentration, andpolymer composition. The measurement signals may include surfaceviscosity and pressure. The long-term risks may include reservoir damageand sand accumulation. The long-term rewards may include sand screeningefficiency and borehole integrity.

In production chemical operations, the short-term rewards may includeincrease in production rate and increase in damage removal rate. Theshort-term risks may include downhole temperature change and reservoirfluid composition change. The control signals may control or affect thepump rate and chemical composition. The measurement signals may includeseparation of reservoir fluids, production rate, and damage removalrate. The long-term risks may include solid clogging. The long-termrewards may include separation capability of reservoir fluids, increasein production rate, and decrease in chemical cost.

In drilling operations, short-term rewards may include weight-on-bit anddrillstring rotations-per-minute. The short-term risks may include aregion of vibration. The long-term rewards may include, maximum doglegseverity, and rate of penetration. The control signals may control oraffect the path taken by the bottomhole assembly.

In hydraulic workover operations, short-term rewards may includeincreased hydraulic workover pipe insertion speed. The short-term risksmay include pressure release. The long-term risks may include incorrecthydraulic workover pipe location. The long-term rewards may includeincreased hydraulic workover pipe insertion speed and accurate hydraulicworkover pipe location. The control signals may control or affect forceof hydraulic workover pipe insertion, speed of hydraulic workover pipeinsertion, type of hydraulic workover pipe, composition of hydraulicworkover pipe, and diameter of hydraulic workover pipe. The measurementsignals may include surface pressure and pipe end location.

In logging operations, short-term risks may include increasedmeasurement noise, and biased measurements. The long-term risks mayinclude inaccurate formation model and inaccurate reservoir model. Thelong-term rewards may include increased logging speed and increasedlogging resolution. The control signals may control or affect selectionof logging tool and speed of logging tool.

In cementing operations, a short-term reward may include increasedcement pumping rate. The short-term risks may include mud bubble, unevencement surface, and poor cement bond. The long-term risks may includeloss of cement integrity and low fracture gradient. The long-termrewards may include increased cement integrity, decreased wait-on-cementtime, and decreased material cost. The control signals may control oraffect cement type and cement composition. Measurement signals mayinclude cement viscosity and cement pump rate.

FIG. 8 is a flow diagram of an illustrative method 800 of optimizingoilfield operations, beginning at 802 and ending at 814. For clarity,the method 800 will be discussed using a hydraulic fracturing example,however, any oilfield operations in the above environments may beoptimized using the optimization method 800. At 804, a formation-basedmodel is analyzed to determine optimized values for a set of decisionvariables. For example, in a hydraulic fracturing operation theformation-based model may include a model of induced fractures, a modelof a reservoir, and a model of surface equipment. The long-term costfunction may be maximized for the long-term reward of stimulatedreservoir volume (SRV), and as such the long-term optimizer may optimizethe decision variables for proppant type, fluid type, gel type, andfracture plan (pump rate, proppant schedule, and the like) for eachstage within the hydraulic fracturing job. The long-term risks used inthe long-term cost function may be compatibility of fracturing fluid andformation and proppant screen-out. As such, the long-term optimizer maytend to select a cheaper fluid that is well-compatible with theformation and a proppant that is unlikely to screen out. At 806, adesired job state is derived from the optimized values. The desired jobstate is passed as constraints to the short-term optimizers.

At 808, measurement signals are obtained from an interface to equipmentand sensors that perform the oilfield operation. These measurementsignals are used to update the model of the oilfield operation andconstruct a current job state to be compared with the desired job stateby the short-term optimizers. For a hydraulic fracturing operation, themeasurement data may include pressure and microseismic activitymeasurements. At 810, a current job state is derived based on themeasurement signals. The short-term optimizer may then minimize theshort term cost function based on a comparison between the desired jobstate and the current job state. Additionally, short-term risks andrewards are taken into account as well. At 812, control signals areprovided by the short-term optimizers that optimize the short-term costfunction. For example, in a hydraulic fracturing operation, if themeasurement data shows that pre-mature screen-out is likely to occur,then the short-term risk will increase and hence the short-termoptimizer will try to reduce the short-term risk by sending a controlsignal to increase the flow rate. As such, the risk and reward analysismay change in real time according to changing conditions.

FIGS. 9A-9C are diagrams of a specific risk and reward scenarios.Specifically, risk and reward scenarios for drilling operations areshown. In FIG. 9A, a well path is shown that avoids a high-riskformation. Here, the long-term optimizer has constrained the short-termoptimizers to avoid the high-risk formation, which poses a long-termrisk to the drilling operation, but has left the method of avoidance(e.g. whether to drill above or below the high-risk region) to theshort-term optimizers. FIGS. 9B and 9C illustrate short-term rewards andrisks for the drilling operation. In terms of short-term rewardsillustrated in FIG. 9B, usually a higher weight-on-bit (WOB) anddrillstring rotations-per-minute (RPM) lead to higherrate-of-penetration (ROP), which is the reward. However, as illustratedin FIG. 9C there exists some operating region where BHA vibration isvery likely to happen. When uncertainties in both RPM and WOB are low,the measured values of RPM and WOB are close to their true values, thusthe high risk region defined is small. If the uncertainties of bothvariables are high, the high-risk region will inflate to reduce thepossibility of vibration occurring. Through continuous minimization ofthe short-term cost function, the short-term optimizer will find theoptimal operating point for drilling.

A system for optimizing a hydraulic fracturing operation includes aninterface to equipment and sensors for performing the hydraulicfracturing operation, wherein the interface supplies control signals tothe equipment and obtains measurement signals from the sensors. Thesystem further includes a short-term optimizer module that derives acurrent job state based at least in part on the measurement signals, andthat further adjusts the control signals to optimize a short-term costfunction, the short-term cost function including a difference betweenthe current job state and a desired job state derived from optimizedvalues of a set of decision variables. The system further includes along-term optimizer module that determines the optimized values based ona long-term cost function, the long-term cost function accounting for atleast a long-term reward and a final state cost.

The short-term optimizer module may derive the current job state basedon the measurement signals and the control signals. Deriving the currentjob state may be performed with an adaptive system model. The system mayallocate portions of risk between the short-term cost function and thelong-term cost function based on dynamic variability of those portions.

A hydraulic fracturing operation optimization method includes analyzinga formation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the hydraulic fracturingoperation. The method further includes deriving a current job statebased at least in part on the measurement signals. The method furtherincludes providing, to the interface, control signals that optimize ashort-term cost function, the short-term cost function including adifference between the current job state and the desired job state, thecontrol signals controlling one or more portions of the hydraulicfracturing operation selected from the group consisting of perforationdensity, borehole diameter, casing diameter, perforation diameter,fracturing fluid composition, proppant composition, gel breakercomposition, pump rate, and proppant schedule.

A system for optimizing a drilling fluid operation includes an interfaceto equipment and sensors for performing the drilling fluid operation,wherein the interface supplies control signals to the equipment andobtains measurement signals from the sensors. The system furtherincludes a short-term optimizer module that derives a current job statebased at least in part on the measurement signals, and that furtheradjusts the control signals to optimize a short-term cost function, theshort-term cost function including a difference between the current jobstate and a desired job state derived from optimized values of a set ofdecision variables. The system further includes a long-term optimizermodule that determines the optimized values based on a long-term costfunction, the long-term cost function accounting for at least along-term reward and a final state cost.

The short-term cost function may include a short-term reward includingrock chips removal rate. The short-term optimizer module may derive thecurrent job state based on the measurement signals and the controlsignals. Deriving the current job state may be performed with anadaptive system model.

A drilling fluid operation optimization method includes analyzing aformation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the drilling fluid operation.The method further includes deriving a current job state based at leastin part on the measurement signals. The method further includesproviding, to the interface, control signals that optimize a short-termcost function, the short-term cost function including a differencebetween the current job state and the desired job state, the controlsignals controlling one or more portions of the drilling fluid operationselected from the group consisting of pump rate, drilling fluidcomposition, fluid addition rate, rock cutting removal rate, andmonitoring equivalent circulating density.

A system for optimizing a well completion operation includes aninterface to equipment and sensors for performing the well completionoperation, wherein the interface supplies control signals to theequipment and obtains measurement signals from the sensors. The systemfurther includes a short-term optimizer module that derives a currentjob state based at least in part on the measurement signals, and thatfurther adjusts the control signals to optimize a short-term costfunction, the short-term cost function including a difference betweenthe current job state and a desired job state derived from optimizedvalues of a set of decision variables. The system further includes along-term optimizer module that determines the optimized values based ona long-term cost function, the long-term cost function accounting for atleast a long-term reward and a final state cost.

The short-term cost function may include a short-term reward includinggravel-packing sand transport speed. The short-term cost function mayinclude a short-term risk created by the current job state selected fromthe group consisting of thin gravel-packing carrier fluid and sand duneeffect. The short-term optimizer module may derive the current job statebased on the measurement signals and the control signals.

A well completion operation optimization method includes analyzing aformation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the well completion operation.The method further includes deriving a current job state based at leastin part on the measurement signals. The method further includesproviding, to the interface, control signals that optimize a short-termcost function, the short-term cost function including a differencebetween the current job state and the desired job state, the controlsignals controlling one or more portions of the well completionoperation selected from the group consisting of pump rate,gravel-packing sand concentration, and polymer composition.

A system for optimizing a production chemical operation includes aninterface to equipment and sensors for performing the productionchemical operation, wherein the interface supplies control signals tothe equipment and obtains measurement signals from the sensors. Thesystem further includes a short-term optimizer module that derives acurrent job state based at least in part on the measurement signals, andthat further adjusts the control signals to optimize a short-term costfunction, the short-term cost function including a difference betweenthe current job state and a desired job state derived from optimizedvalues of a set of decision variables. The system further includes along-term optimizer module that determines the optimized values based ona long-term cost function, the long-term cost function accounting for atleast a long-term reward and a final state cost.

The short-term cost function may include a short-term reward selectedfrom the group consisting of increase in production rate and increase indamage removal rate. The short-term cost function may include ashort-term risk created by the current job state selected from the groupconsisting of downhole temperature change and reservoir fluidcomposition change. The short-term optimizer module may derive thecurrent job state based on the measurement signals and the controlsignals.

A production chemical operation optimization method includes analyzing aformation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the production chemicaloperation. The method further includes deriving a current job statebased at least in part on the measurement signals. The method furtherincludes providing, to the interface, control signals that optimize ashort-term cost function, the short-term cost function including adifference between the current job state and the desired job state, thecontrol signals controlling one or more portions of the productionchemical operation selected from the group consisting of pump rate andchemical composition.

A system for optimizing a drilling operation includes an interface toequipment and sensors for performing the drilling operation, wherein theinterface supplies control signals to the equipment and obtainsmeasurement signals from the sensors. The system further includes ashort-term optimizer module that derives a current job state based atleast in part on the measurement signals, and that further adjusts thecontrol signals to optimize a short-term cost function, the short-termcost function including a difference between the current job state and adesired job state derived from optimized values of a set of decisionvariables. The system further includes a long-term optimizer module thatdetermines the optimized values based on a long-term cost function, thelong-term cost function accounting for at least a long-term reward and afinal state cost.

The short-term cost function may include a short-term reward selectedfrom the group consisting of weight-on-bit and drillstringrotations-per-minute. The short-term cost function may include ashort-term risk created by the current job state including a region ofvibration. The short-term optimizer module may derive the current jobstate based on the measurement signals and the control signals. Thecurrent job state may be performed with an adaptive system model. Thesystem may allocate portions of risk between the short-term costfunction and the long-term cost function based on dynamic variability ofthose portions. The long-term cost function may include one or morelong-term rewards selected from the group consisting of total lengthdrilled, maximum dogleg severity, and rate of penetration.

A drilling operation optimization method includes analyzing aformation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the drilling operation. Themethod further includes deriving a current job state based at least inpart on the measurement signals. The method further includes providing,to the interface, control signals that optimize a short-term costfunction, the short-term cost function including a difference betweenthe current job state and the desired job state, the control signalscontrolling a trajectory of a bottomhole assembly.

A system for optimizing a hydraulic workover operation includes aninterface to equipment and sensors for performing the hydraulic workoveroperation, wherein the interface supplies control signals to theequipment and obtains measurement signals from the sensors. The systemfurther includes a short-term optimizer module that derives a currentjob state based at least in part on the measurement signals, and thatfurther adjusts the control signals to optimize a short-term costfunction, the short-term cost function including a difference betweenthe current job state and a desired job state derived from optimizedvalues of a set of decision variables. The system further includes along-term optimizer module that determines the optimized values based ona long-term cost function, the long-term cost function accounting for atleast a long-term reward and a final state cost.

The short-term cost function may include a term representing ashort-term reward including increased hydraulic workover pipe insertionspeed. The short-term cost function may include a short-term riskcreated by the current job state comprising pressure release. Theshort-term optimizer module may derive the current job state based onthe measurement signals and the control signals. Deriving the currentjob state may be performed with an adaptive system model. The system mayallocate portions of risk between the short-term cost function and thelong-term cost function based on dynamic variability of those portions.The long-term cost function may include long-term risks includingincorrect hydraulic workover pipe location. The long-term cost functionmay include one or more long-term rewards selected from the groupconsisting of increased hydraulic workover pipe insertion speed andaccurate hydraulic workover pipe location.

A hydraulic workover operation optimization method includes analyzing aformation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the hydraulic workoveroperation. The method further includes deriving a current job statebased at least in part on the measurement signals. The method furtherincludes providing, to the interface, control signals that optimize ashort-term cost function, the short-term cost function including adifference between the current job state and the desired job state, thecontrol signals controlling one or more portions of the hydraulicworkover operation selected from the group consisting of force ofhydraulic workover pipe insertion, speed of hydraulic workover pipeinsertion, type of hydraulic workover pipe, composition of hydraulicworkover pipe, and diameter of hydraulic workover pipe.

The long-term cost function may include one or more long-term rewardsselected from the group consisting of increased hydraulic workover pipeinsertion speed and accurate hydraulic workover pipe location. Thelong-term cost function may include long-term risk including incorrecthydraulic workover pipe location. The method may include allocatingportions of risk between the short-term cost function and the long-termcost function based on dynamic variability of those portions.

A system for optimizing a logging operation includes an interface toequipment and sensors for performing the logging operation, wherein theinterface supplies control signals to the equipment and obtainsmeasurement signals from the sensors. The system further includes ashort-term optimizer module that derives a current job state based atleast in part on the measurement signals, and that further adjusts thecontrol signals to optimize a short-term cost function, the short-termcost function including a difference between the current job state and adesired job state derived from optimized values of a set of decisionvariables. The system further includes a long-term optimizer module thatdetermines the optimized values based on a long-term cost function, thelong-term cost function accounting for at least a long-term reward and afinal state cost.

The short-term cost function may include a short-term risk created bythe current job state selected from the group consisting of increasedmeasurement noise and biased measurements. The short-term optimizermodule may derive the current job state based on the measurement signalsand the control signals. Deriving the current job state may be performedwith an adaptive system model. The system may allocate portions of riskbetween the short-term cost function and the long-term cost functionbased on dynamic variability of those portions. The long-term costfunction may include long-term risks selected from the group consistingof inaccurate formation model and inaccurate reservoir model. Thelong-term cost function may include one or more long-term rewardsselected from the group consisting of increased logging speed andincreased logging resolution.

A logging operation optimization method includes analyzing aformation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the logging operation. Themethod further includes deriving a current job state based at least inpart on the measurement signals. The method further includes providing,to the interface, control signals that optimize a short-term costfunction, the short-term cost function including a difference betweenthe current job state and the desired job state, the control signalscontrolling one or more portions of the logging operation selected fromthe group consisting of selection of logging tool and speed of loggingtool.

A system for optimizing a cementing operation includes an interface toequipment and sensors for performing the cementing operation, whereinthe interface supplies control signals to the equipment and obtainsmeasurement signals from the sensors. The system further includes ashort-term optimizer module that derives a current job state based atleast in part on the measurement signals, and that further adjusts thecontrol signals to optimize a short-term cost function, the short-termcost function including a difference between the current job state and adesired job state derived from optimized values of a set of decisionvariables. The system further includes a long-term optimizer module thatdetermines the optimized values based on a long-term cost function, thelong-term cost function accounting for at least a long-term reward and afinal state cost.

The short-term cost function may include a short-term reward includingincreased cement pumping rate. The short-term cost function may includea short-term risk created by the current job state selected from thegroup consisting of mud bubble, uneven cement surface, and poor cementbond. The short-term optimizer module may derive the current job statebased on the measurement signals and the control signals. Deriving thecurrent job state may be performed with an adaptive system model. Thesystem may allocate portions of risk between the short-term costfunction and the long-term cost function based on dynamic variability ofthose portions. The long-term cost function may include long-term risksselected from the group consisting of loss of cement integrity and lowfracture gradient. The long-term cost function may include one or morelong-term rewards selected from the group consisting of increased cementintegrity, decreased wait-on-cement time, and decreased material cost.

A cementing operation optimization method includes analyzing aformation-based model to determine optimized values for a set ofdecision variables subject to a long-term cost function including atleast a long-term reward and a final state cost. The method furtherincludes deriving a desired job state from the optimized values. Themethod further includes obtaining measurement signals from an interfaceto equipment and sensors for performing the cementing operation. Themethod further includes deriving a current job state based at least inpart on the measurement signals. The method further includes providing,to the interface, control signals that optimize a short-term costfunction, the short-term cost function including a difference betweenthe current job state and the desired job state, the control signalscontrolling one or more portions of the cementing operation selectedfrom the group consisting of cement type and cement composition.

The long-term cost function may include long-term risks selected fromthe group consisting of loss of cement integrity and low fracturegradient. The long-term cost function may include one or more long-termrewards selected from the group consisting of increased cementintegrity, decreased wait-on-cement time, and decreased material cost.The method of claim may include allocating portions of risk between theshort-term cost function and the long-term cost function based ondynamic variability of those portions.

While the present disclosure has been described with respect to alimited number of embodiments, those skilled in the art will appreciatenumerous modifications and variations therefrom. It is intended that theappended claims cover all such modifications and variations.

What is claimed is:
 1. A system for optimizing a completion operation, comprising: an interface to equipment and sensors for performing the completion operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors; at least one short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function comprising: short-term risks and short-term rewards; and a difference between the current job state and a desired job state derived from optimized values of a set of decision variables; and a long-term optimizer module, coupled to the at least one short-term optimizer module, that determines the optimized values based on a long-term cost function, the long-term cost function comprising long-term risks and long-term rewards and accounting for at least a long-term reward and a final state cost, wherein: the control signals control completion operation equipment; the short term-risks are promoted to long-term risks when the short-term risks persist over a threshold time period; and the short-term rewards and risks and the long-term rewards and risk comprise non-financial rewards and risks.
 2. The system of claim 1, wherein the short-term optimizer module derives the current job state based on the measurement signals and the control signals.
 3. The system of claim 1, wherein the difference between the current job state and the desired job state is weighted by weighting factors.
 4. The system of claim 3, wherein the weighting factors are adjusted according to output uncertainty.
 5. The system of claim 1, wherein the completion operation comprises a cementing operation.
 6. The system of claim 5, wherein the short-term reward comprises an increased cement pumping rate.
 7. The system of claim 5, wherein the short-term risk is created by the current job state selected from the group consisting of mud bubble, uneven cement surface, and poor cement bond.
 8. The system of claim 5, wherein the long-term risk is selected from the group consisting of loss of cement integrity and low fracture gradient.
 9. The system of claim 5, wherein the long-term reward is selected from the group consisting of increased cement integrity, decreased wait-on-cement time, and decreased material cost.
 10. The system of claim 1, wherein the completion operation comprises a well completion operation.
 11. The system of claim 10, wherein the short-term reward comprises gravel-packing sand transport speed.
 12. The system of claim 10, wherein the short-term risk is created by the current job state selected from the group consisting of thin gravel-packing carrier fluid and sand dune effect.
 13. A completion operation optimization method, comprising: analyzing, by a long-term optimizer module, a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function comprising at least a long-term reward, long-term risk, and a final state cost; deriving a desired job state from the optimized values; obtaining measurement signals from an interface to equipment and sensors for performing the completion operation; deriving a current job state based at least in part on the measurement signals; and providing, to the interface, control signals that optimize a short-term cost function, by at least one short-term optimizer module coupled to the long-term optimizer module, the short-term cost function comprising: short-term risks and short-term rewards, and a difference between the current job state and the desired job state, wherein: the control signals control completion operation equipment; the short-term risks are promoted to long-term risks when the short-term risks persist over a threshold time period; and the short-term rewards and risks and the at least one long-term reward and risk comprise non-financial rewards and risks.
 14. The method of claim 13, further comprising allocating portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions.
 15. The method of claim 13, wherein the completion operation comprises a cementing operation.
 16. The method of claim 15, wherein the control signals control one or more portions of the cementing operation selected from the group consisting of cement type and cement composition.
 17. The method of claim 15, wherein the long-term risk is selected from the group consisting of loss of cement integrity and low fracture gradient.
 18. The method of claim 13, wherein the completion operation comprises a well completion operation.
 19. The method of claim 18, wherein the control signals control one or more portions of the well completion operation selected from the group consisting of pump rate, gravel-packing sand concentration, and polymer composition.
 20. The method of claim 18, wherein the short-term reward comprises comprising gravel-packing sand transport speed. 